Publications

8 / 3,733 publications found.


  •  Queeney, J., Paschalidis, I.C., Cassandras, C.G., "Generalized Policy Improvement Algorithms with Theoretically Supported Sample Reuse", arXiv, October 2024.
    BibTeX arXiv
    • @article{Queeney2024oct,
    • author = {Queeney, James and Paschalidis, Ioannis Ch. and Cassandras, Christos G.}},
    • title = {Generalized Policy Improvement Algorithms with Theoretically Supported Sample Reuse},
    • journal = {arXiv},
    • year = 2024,
    • month = oct,
    • url = {https://arxiv.org/abs/2206.13714}
    • }
  •  Cai, X., Queeney, J., Xu, T., Datar, A., Pan, C., Miller, M., Flather, A., Osteen, P.R., Roy, N., Xiao, X., How, J.P., "PIETRA: Physics-Informed Evidential Learning for Traversing Out-of-Distribution Terrain", arXiv, September 2024.
    BibTeX arXiv
    • @article{Cai2024sep,
    • author = {Cai, Xiaoyi and Queeney, James and Xu, Tong and Datar, Aniket and Pan, Chenhui and Miller, Max and Flather, Ashton and Osteen, Philip R. and Roy, Nicholas and Xiao, Xuesu and How, Jonathan P.}},
    • title = {PIETRA: Physics-Informed Evidential Learning for Traversing Out-of-Distribution Terrain},
    • journal = {arXiv},
    • year = 2024,
    • month = sep,
    • url = {https://www.arxiv.org/abs/2409.03005}
    • }
  •  Giammarino, V., Queeney, J., Paschalidis, I.C., "Visually Robust Adversarial Imitation Learning from Videos with Contrastive Learning", arXiv, June 2024.
    BibTeX arXiv
    • @article{Giammarino2024jun2,
    • author = {Giammarino, Vittorio and Queeney, James and Paschalidis, Ioannis Ch.}},
    • title = {Visually Robust Adversarial Imitation Learning from Videos with Contrastive Learning},
    • journal = {arXiv},
    • year = 2024,
    • month = jun,
    • url = {https://arxiv.org/abs/2407.12792}
    • }
  •  Giammarino, V., Queeney, J., Paschalidis, I.C., "Adversarial Imitation Learning from Visual Observations using Latent Information", Transactions on Machine Learning Research (TMLR), June 2024.
    BibTeX TR2024-068 PDF
    • @article{Giammarino2024jun,
    • author = {Giammarino, Vittorio and Queeney, James and Paschalidis, Ioannis Ch.},
    • title = {Adversarial Imitation Learning from Visual Observations using Latent Information},
    • journal = {Transactions on Machine Learning Research (TMLR)},
    • year = 2024,
    • month = jun,
    • issn = {2835-8856},
    • url = {https://www.merl.com/publications/TR2024-068}
    • }
  •  Chen, Y., Giammarino, V., Queeney, J., Paschalidis, I.C., "Provably Efficient Off-Policy Adversarial Imitation Learning with Convergence Guarantees", arXiv, May 2024.
    BibTeX arXiv
    • @article{Chen2024may2,
    • author = {Chen, Yilei and Giammarino, Vittorio and Queeney, James and Paschalidis, Ioannis Ch.}},
    • title = {Provably Efficient Off-Policy Adversarial Imitation Learning with Convergence Guarantees},
    • journal = {arXiv},
    • year = 2024,
    • month = may,
    • url = {https://arxiv.org/abs/2405.16668}
    • }
  •  Queeney, J., Ozcan, E.C., Paschalidis, I.C., Cassandras, C.G., "Optimal Transport Perturbations for Safe Reinforcement Learning with Robustness Guarantees", Transactions on Machine Learning Research (TMLR), April 2024.
    BibTeX TR2024-037 PDF
    • @article{Queeney2024apr,
    • author = {Queeney, James and Ozcan, Erhan Can and Paschalidis, Ioannis Ch. and Cassandras, Christos G.},
    • title = {Optimal Transport Perturbations for Safe Reinforcement Learning with Robustness Guarantees},
    • journal = {Transactions on Machine Learning Research (TMLR)},
    • year = 2024,
    • month = apr,
    • issn = {2835-8856},
    • url = {https://www.merl.com/publications/TR2024-037}
    • }
  •  Ozcan, E.C., Giammarino, V., Queeney, J., Paschalidis, I.C., "A Model-Based Approach for Improving Reinforcement Learning Efficiency Leveraging Expert Observations", arXiv, February 2024.
    BibTeX arXiv
    • @article{Ozcan2024feb,
    • author = {Ozcan, Erhan Can and Giammarino, Vittorio and Queeney, James and Paschalidis, Ioannis Ch.},
    • title = {A Model-Based Approach for Improving Reinforcement Learning Efficiency Leveraging Expert Observations},
    • journal = {arXiv},
    • year = 2024,
    • month = feb,
    • url = {https://arxiv.org/abs/2402.18836}
    • }
  •  Queeney, J., Benosman, M., "Risk-Averse Model Uncertainty for Distributionally Robust Safe Reinforcement Learning", Advances in Neural Information Processing Systems (NeurIPS), A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine, Eds., December 2023, pp. 1659-1680.
    BibTeX TR2023-143 PDF
    • @inproceedings{Queeney2023dec,
    • author = {{Queeney, James and Benosman, Mouhacine}},
    • title = {Risk-Averse Model Uncertainty for Distributionally Robust Safe Reinforcement Learning},
    • booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    • year = 2023,
    • editor = {A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine},
    • pages = {1659--1680},
    • month = dec,
    • publisher = {Curran Associates, Inc.},
    • url = {https://www.merl.com/publications/TR2023-143}
    • }